arXiv:2606.26118v1 Announce Type: cross Abstract: We work towards measuring both AI adoption and the capability of AI to perform discrete labor tasks across various occupations. To measure adoption, we develop an open-source economic index that uses publicly available user-LLM chat data and O*NET tasks to replicate studies produced by frontier AI labs, finding that occupations in the finance, computer science, and arts sectors are those with the highest adoption rates. To measure capabilities, we build a system that generates benchmark scenarios grounded in O*NET occupations, tasks, and model-
The development of an open-source economic index for AI adoption and capability emerges now due to increasing availability of public LLM chat data and the need for standardized, transparent metrics for AI's economic impact.
This index offers a crucial open-source benchmark for understanding AI's real-world adoption and capabilities across various occupations, providing transparent insights previously limited to frontier AI labs.
The ability to independently and transparently measure AI adoption and capability shifts from proprietary models to an open-source framework, impacting how industries and governments assess the economic effects of AI.
- · AI researchers
- · Economists
- · Policymakers
- · Open-source communities
- · Proprietary AI impact assessment firms
- · Organizations relying on opaque AI adoption metrics
The index will allow more granular and comparable cross-industry analysis of AI's economic integration.
Increased transparency in AI adoption data could influence investment patterns and educational reforms toward sectors with higher AI engagement.
Standardized open-source metrics could lead to international agreements or policies shaped by a common understanding of AI's economic footprint.
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Read at arXiv cs.LG